Identifying threats based on hierarchical classification

ABSTRACT

A system and a method are disclosed for identifying network threats based on hierarchical classification. The system receives packet flows from a data network and determines flow features for the received packet flows based on data from the packet flows. The system also classifies each packet flow into a flow class based on flow features of the packet flow. Based on a criterion, the system selects packet flows from the received packet flows and places the selected packet flows into an event set that represents an event on the network. The system determines event set features for the event set based on the flow features of the selected packet flows. The system then classifies the event set into a set class based on the determined event set features. Based on the set class, the computer system may report a threat incident on an internetworking device that originated the selected packet flows.

BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. §119(e) ofprovisional application 61/994,344, filed May 16, 2014, the entirecontents of which is hereby incorporated by reference for all purposesas if fully set forth herein.

TECHNICAL FIELD

The present disclosure generally relates to improved methods, computersoftware and computer hardware in the field of security of computernetworks. The disclosure relates more specifically to improvedcomputer-based network security devices and computer-implementedtechniques that can perform classifying threats in computer networks.

BACKGROUND

The approaches described in this section could be pursued, but are notnecessarily approaches that have been previously conceived or pursued.Therefore, unless otherwise indicated herein, the approaches describedin this section are not prior art to the claims in this application andare not admitted to be prior art by inclusion in this section.

Network security incidents are composed of several events, steps oractions that an attacker may take to compromise a targeted network andextract valuable intellectual property or personal data, or performmalicious reconfiguration operations. These steps may include scanningof potential targets, initial infection, library download, orcommunication with a command and control server.

Traditionally, signature-based security devices, firewalls, oranti-viruses are deployed to detect such threats. However,signature-based algorithms simply compare a byte sequence that has beendetected to stored byte-sequences corresponding to known threats, whichmay be in a database. Thus, if a new threat has not yet been analyzedand recorded into the database, the signature based algorithm may notidentify the new threat. Furthermore, if a threat has the ability tochange, the signature-based algorithms may again fail to identify thethreat because a current signature of the threat may be different than astored signature of the same threat that was recorded earlier. Thus,polymorphic malware, zero-day attacks by threats that are novel orpreviously unseen, or other types of advanced persistent network threatsare usually not detected or blocked by signature-based securityalgorithms.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example operating environment upon which anembodiment may be implemented.

FIG. 2 is a block diagram that illustrates an embodiment of ahierarchical classification system (HCS).

FIG. 3 is a flow diagram that depicts a process of hierarchicallyclassifying packet flows into network threats, in an embodiment.

FIG. 4 depicts an example of packet flow classification on differentlevels of hierarchy, in an embodiment.

FIG. 5 depicts an URL string partition, in an embodiment.

FIG. 6 is a block diagram that illustrates a computer system upon whichan embodiment of the system may be implemented.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Identifying threats based on hierarchical classification is described.In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,to one skilled in the art that the present disclosure may be practicedwithout these specific details. In other instances, well-knownstructures and devices are shown in block diagram form in order to avoidunnecessarily obscuring the present disclosure.

Embodiments are described herein according to the following outline:

-   -   1.0 General Overview    -   2.0 Example Operating Environment    -   3.0 Structural Overview    -   4.0 Functional Description        -   4.1 Flow Level Analysis        -   4.2 Complex Features        -   4.3 Flow Level Classification        -   4.4 Event Description        -   4.5 Incident Description    -   5.0 Implementation Mechanisms—Hardware Overview    -   6.0 Extensions and Alternatives

1.0 GENERAL OVERVIEW

Identifying threats based on hierarchical classification is described.In an embodiment, a computer system receives a plurality of packet flowsfrom a data network. Based on a criterion, the computer system selectspacket flows from the plurality of packet flows and places the selectedpacket flows into a set. The computer system determines, for each packetflow in the set, a flow feature associated with that packet flow basedon data from that packet flow, and classifies each packet flow into aflow class based on the flow feature. The computer system furtherdetermines set features for the set based on the flow features that areassociated with the selected packet flows of the set. The computersystem then classifies the set into a set class based on the determinedset features. Based on the set class, the computer system may report athreat incident on an internetworking device associated with theselected packet flows. In other embodiments, the disclosure encompassesa method that carries out the foregoing steps using one or morecomputing devices.

In an embodiment, a hierarchical classification system (HCS) analyzesnetwork packet flows with statistical and machine learning algorithms toidentify and report novel security threat incidents from events on acomputing device. As referred to herein, “packet flow” describes packettraffic that comprises network packets with the same source anddestination internet addresses. In some embodiment, network packets of apacket flow may also have the same source port number, the samedestination port number and the same protocol. The events are identifiedfrom the packet flows, classified, and combined into incidents based onautomatically-recognized relationships. Since the system analyzesvarious features of network traffic on different levels of granularity,from packet flows to events to incidents, the system may correlate fastclassifiers at the lower levels like the packet flow level with complex,more computationally intensive classifiers at the higher levels likeincident level to help identify threats more effectively. At everylevel, the classifiers utilize statistical properties derived from thenetwork traffic at the granularity that is specific to each level aswell as information gained at the lower levels. The system then mayclassify network traffic starting with simple classifiers designed toprocess millions network connections per minute and ending with modelingthe behavior of each potentially infected computing device. Theresulting classification data may be used to facilitate more efficient,faster recognition of threats in networks and to initiate remediationefforts.

2.0 EXAMPLE OPERATING ENVIRONMENT

FIG. 1 illustrates an example operating environment upon which anembodiment may be implemented. In FIG. 1, network traffic 152 betweenenterprise main campus 110 and enterprise remote branches 120, 130 isdepicted. The computer system 123 of branch 120 may initiatecommunication with computer system 113 of main campus 110 through aproxy device 125 that is connected to Internet 150. Packet flows 154 ofthe network communication from computer system 123 are routed byinternetworking devices in Internet 150 to a gateway computer system115. Once received by gateway computer system 115, the communication isthen routed to a destination main campus computer system 113.

In an example of processing a network threat incident, computer system140, also connected to Internet 150, may be a source of a networkattack. The network attack may have been able to penetrate branchcomputer system 123. Thus, the packet flows from computer system 123 tomain campus computer system 113 may contain malicious network attackpackets. In order to detect the network threat, HCS logic 200 may bepart of gateway system 115, in an embodiment. As the packet flows 154are received by gateway system 115, the HCS logic 200 may analyze thepacket flows 154 using the techniques described herein.

In another embodiment, the HCS logic 200 may be in a separate computersystem such as central server computer 117. In this configuration, theHCS logic 200 may import packet flow log data or files (“logs”) andanalyze them rather than receiving packet flows 154 directly fromnetwork. For example, the HCS logic 200 may receive logs from gatewaysystem 115 as well as other systems on the network, such as proxy 125.The logs from proxy 125 may also contain the packet flows of the initialnetwork attack by computer system 140 on computer system 123, since thepacket flows containing the attack should have passed through proxy 125.Thus, using techniques described herein, the HCS logic 200 may detectthe network threat incident on computer system 123 and also may tracethe incident to the originating computer system 140.

3.0 STRUCTURAL OVERVIEW

FIG. 2 depicts an embodiment of the HCS logic. In one embodiment, HCSlogic 200 may comprise packet flow importer 210, feature analyzer 220,aggregator 230, classifier 240, threat reporter 250 and datastore 260.For purposes of brevity, the terms packet flow importer 210, featureanalyzer 220, aggregator 230, classifier 240, threat reporter 250 anddatastore 260 are used and each such unit may be implemented, in variousembodiments, using one or more computer programs or other softwareelements, one or more units of digital logic, a special-purposecomputer, or other computing elements.

In an embodiment, the packet flow importer 210 may be configured to becoupled to a network interface to receive packet flows from network.Once received, the packet flows may be analyzed on-line or stored intothe datastore 260 for future analysis. In an alternative embodiment,packet flow importer 210 may import log data such as log files thatcontain packet flow data. Such log files may be from variousinternetworking devices that the HCS logic 200 can access, such as proxy125 or gateway system 115. Packet flow importer 210 may analyze theimported log files to determine flow features before or after storingthe packet flow data into datastore 260. “Packet flow data,” as usedherein, is data that describes packet flow information or derives frompacket flow information and typically comprises a five-tuple or otherset of values that are common to all packets that are related in a flow,such as source address, destination address, source port, destinationport, and protocol value.

In an embodiment, datastore 260 may be a data storage device and/or adatabase that has a particular database schema. Thus, packet flowimporter 210 and other components of HCS logic 200, using datastore 260,may convert packet flow data to the particular database schema to storethe data in the database of datastore 260. In an alternative embodiment,datastore 260 may comprise a file system with one or more structureddata files that may have various formats or schemas. For example,datastore 260 may store packet flow data in log files similar to orexactly matching log files received by packet flow importer 210, whileother data may be stored in XML files conforming to a particular schema.The exact type of datastore, schema, or combination of schemas are notcritical.

In an embodiment, feature analyzer 220 is configured to determinefeatures of packet flows or any aggregation of packet flows. Featureanalyzer 220 may receive packet flow data from packet flow importer 210or may select packet flow data from datastore 260 to perform analysis ofthe received packet flows. Feature analyzer 220 may first perform abasic analysis to extract native flow features. “Native flow feature”refers herein to a feature of a packet flow that is intrinsic to thepacket flow and generally may be extracted from packet flow fields. Alist of example native flow features is in Table 1 below. Using nativeflow features and other packet flow data, feature analyzer 220 mayperform calculations to determine complex features. A “complex feature”refers herein to a feature of packet flow that may not be readilyavailable from native flow fields of a packet flow. A complex featuremay be calculated based on statistical features of one or more nativeflow features. Feature analyzer 220 may also determine other types offeatures such as event and incident features as described in more detailbelow. Feature analyzer 220 may retrieve the packet flow data necessaryfor calculations from datastore 260 or receive the data directly fromother components of HCS logic 200. Once calculated, feature analyzer 220may store features into datastore 260.

In an embodiment, aggregator 230 is configured to hierarchicallyaggregate packet flows into multiple hierarchical levels of sets andgroups. Aggregator 230 may use various criteria to aggregate packetflows. Such criteria may include time periods, origin devices, events onthe origin devices or any combination of thereof. Aggregator 230 mayalso use various features of packet flow data in the aggregation.Aggregator 230 may retrieve packet flow data for aggregations fromdatastore 260 or receive the data directly from other components of HCSlogic 200. Once aggregated, aggregator 230 may store the new aggregationinto datastore 260.

In an embodiment, classifier 240 is configured to classify packet flows,sets and groups of packet flows into categories and classifications. Theclassifications may include threat levels such as malicious, fault,possible policy violation, warning, legitimate and OK. The categoriesmay include the type or behavior of network traffic that a packet flowor an aggregation of packet flows represents. Examples of categories mayinclude multimedia streaming, rich site summary (RSS) feed, generateddomain traffic and many others.

Classifier 240 may retrieve packet flow data for classification fromdatastore 260 or receive the data directly from other components of HCSlogic 200. Once classified, classifier 240 may store the classificationsinto datastore 260.

In an embodiment, threat reporter 250 is configured to reportclassifications of packet flow data to computer display devices, whichmay be used by users of HCS logic 200, in an embodiment. Threat reporter250 may report the classifications for any level of aggregation from asingle packet flow to a network incident. The reports may assist in theanalysis of classifications and understanding of classification ofpreviously undetected malware.

4.0 FUNCTIONAL DESCRIPTION

FIG. 3 is a flow diagram that depicts a process of hierarchicallyclassifying packet flows into network threats, in an embodiment. Atblock 310, packet flows are received by HCS logic, such as HCS logic(FIG. 1, FIG. 2). The HCS logic may then select all or part of thereceived packet flows for analysis and classification at block 315. In arelated embodiment, the HCS logic may periodically select a batch ofreceived packet flows for analysis, where each batch contains packetflows corresponding to the selection period. Any timestamp associatedwith a packet flow may be used to determine whether the packet flowcorresponds to the selection period. In one embodiment, such timestampmay be origination timestamp of the packet flow, and in anotherembodiment, it may be the timestamp of the receipt of the packet flow bythe HCS logic or the timestamp the packet flow is recorded in a log filethat the HCS logic has imported.

FIG. 4 depicts an example of packet flow classification on differentlevels of flow hierarchy, in an embodiment. In FIG. 4, the HCS logicreceives packet flows 410 either from a log file or network interface.The HCS logic then selects, for analysis, a particular batch 415 thatcorresponds to all the packet flows with timestamps within a particulartime period, such as five minutes. The particular time period may varyin different embodiments or according to configuration data in the sameembodiment. The HCS logic continues to select for analysis the nextbatch of packet flows until all packet flows in the log files have beenanalyzed. Alternatively, the HCS logic may continuously select batchesof packet flows as new packet flows are received by the system, asdepicted by batches on time axis 405 in FIG. 4.

In an embodiment, the packet flow importer 210 of the HCS logic 200 mayperform block 310 and store the received packet flows into datastore260.

4.1 Flow Level Analysis

The HCS logic may be configured to analyze and classify individualpacket flows on packet flow level, in an embodiment. At block 325 ofFIG. 3, the HCS logic may extract native flow features of packet flowsthat provide the HCS logic with information to classify a packet flow.As described in Table 1, the process may extract intrinsic features suchas flow duration or number of bytes transferred. In addition tointrinsic features, the native flow features may include protocolspecific features. For example, HTTP header information may be extractedfrom HTTP protocol packet flows. Table 1 provides a sample of HTTPspecific features, but other protocols and protocol features may beextracted for the analysis.

In another embodiment, the feature analyzer 220 of the HCS logic 200 mayperform block 325 to extract native flow features of packet flows.Feature analyzer 220 may then store the extracted features in datastore260 in association with the corresponding packet flows.

TABLE 1 Sample Native Flow Features Feature Name Description URL Originor destination URL of the packet flow such as Host field in HTTPprotocol. Flow duration Time duration between the first packet receivedin a flow till the last packed received in the flow Client-servertraffic size Number of bytes transferred from client to serverServer-client traffic Number of bytes transferred from server to clientUser agent Browser or application description related to the packet flowReferrer URL of the redirection that generated the packet flow.MIME-type Content type of packet flow. Status HTTP response status codes

4.2 Complex Features

In an embodiment, at block 330, complex features of a packet flow may becalculated based on the native features of the packet flow. For example,the HCS logic 200 may apply a set of functions to native flow featuresto calculate complex features. In a related embodiment in which theextracted packet flows are using a protocol that utilizes an URL, suchas HTTP protocol, statistical features may be calculated for each of thepacket flow or a group/set of packet flows that contain the same URLinformation. The complex features are calculated by applying a set offunctions on the URL string or any portion of the URL string.

FIG. 5 depicts a URL string partition, in an embodiment. A URL stringmay be decomposed into a protocol, domain, path, file name, filefragment and query strings. A protocol string, such as protocol 510, mayhave values that denote various application protocols of packet flows,such as “http” for the HTTP protocol or “ftp” for the FTP protocol.Second-level domain string 520 in combination with top-level domain(TLD) 530 strings denote the domain name of a host computer that the URLstring references. The actual network address of the host computer maybe accessed by requesting the network address corresponding tosecond-level domain 520 and TLD 530 from a computer hosting a nameresolution server, such as a DNS server. Path 540 and file name 550strings denote the path to the file name on the host computer system tobe invoked using the URL string. Query string 560 denotes the meta-datapassed in to the file and fragment 570 denotes an index to a particularportion of the file.

In an embodiment, a URL string or a partition of the URL string asdescribed above or any combination of partitions of the URL string maybe used as an input string to calculate the values of one or more of thefollowing complex features.

Length—describes the number of characters in the string, in anembodiment.

Consonant to vowel change ratio—describes the frequency of changesbetween consonants and vowels, in an embodiment. The equation belowdescribes the ratio r_(v), where l(c_(i)) describes the length featureof the input string c_(i).

${r_{v}\left( c_{i} \right)} = \frac{{number}\mspace{14mu} {of}\mspace{14mu} {changes}\mspace{14mu} {from}\mspace{14mu} {consonant}\mspace{14mu} {to}\mspace{14mu} {vowel}}{l\left( c_{i} \right)}$

ASCII metric—describes whether the input string contains any charactersthat are in the UTF-8 character set but not in the ASCII character set,in an embodiment. The ASCII metric feature may be a Boolean valueindicating whether any character outside of ASCII character set existsin the input string. The metric may have a value of FALSE when anycharacter from the input string is outside the ASCII range of charactersand may have a value of TRUE otherwise.

Maximum character occurrence ratio—describes the maximum number of therecurrence of a character in the input string as compared to the lengthof the string, in an embodiment. This ratio feature may be calculated bymeasuring a maximum number of recurrences of any character in the inputstring and by dividing the maximum number by the length of the inputstring.

Maximum character type occurrence ratio—describes the maximum number ofthe recurrence of a character type in the input string as compared tothe length of the string, in an embodiment. An example of character typeis a letter character, a capital letter character, a number character ora special character. In a related embodiment, this ratio feature may becalculated by measuring the maximum number of recurrences of any of thecharacter types and dividing the maximum number by the length of theinput string.

URL based form addressability—describes whether the input stringcontains URL addressable forms, in an embodiment. Special characters maybe used as separators in URL addressable forms and views. Thus therepetitive occurrence of special characters may indicate that a pageassociated with the URL contains forms and views. In a relatedembodiment, the feature may be calculated by counting the number ofoccurrences of special characters such as ‘=’ and ‘&’.

Popular trigram probability—describes whether trigrams within the inputstring are similar to those in well-known domain names, in anembodiment. An existing repository of well-known domains, such as theALEXA service, may be used. In a related embodiment, the most populartrigrams in the domains of a domain repository may be ranked. Then, eachof component trigrams from the input string may be compared to theranked list of the most popular trigrams of the repository. If acomponent trigram matches, then a probability value is assigned to thecomponent trigram based on the rank of the matched popular trigram. Inthis approach, a higher rank of the matched trigram results in assigninga greater probability value to the component trigram. To calculate thefeature, an average may be taken of all probabilities assigned to thecomponent trigrams of the input string. For example, a ranking of 1 to1,000 is selected for trigrams in the repository. To rank all therepository trigrams, each trigrams probability may be calculated usingthe following equation:

${{p_{t}\left( {t(A)} \right)} = \frac{{number}\mspace{14mu} {of}\mspace{14mu} {t(A)}\mspace{14mu} {occurrences}\mspace{14mu} {in}\mspace{14mu} {Alexa}}{{number}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {trigrams}\mspace{14mu} {in}\mspace{14mu} {Alexa}}},$

-   -   where t(A) represents a trigram of domains in the repository.

Then a rank may be assigned to each domain trigram describing thetrigram frequency. The rank may be described by:

∀i,j≦|A|:p _(t)(t ^((i))(A))≧p _(t)(t ^((j))(A))

i≦j,

where p_(t)(t^((i))(A)) denotes i-th most-frequent trigram from therepository, |A| is a number of trigrams in the repository, and iε{1, . .. , 1000}.

The ranking probability of each component trigram in the input string,t(d), may then be calculated as:

${{\hat{p}}_{t}\left( {t(d)} \right)} = \left\{ {\begin{matrix}{1 - {\left( {i - 1} \right) \cdot 10^{- 4}}} & {{{\exists{i \leq 1000}}:{t(d)}} = {t^{(i)}(A)}} \\0 & {otherwise}\end{matrix},} \right.$

where {circumflex over (p)}_(t)(t(d)) is a ranking probability of t(d)component trigram in the input string. The feature may be calculated byaveraging of all ranking probabilities {circumflex over (p)}_(t)(t(d)).In a related embodiment, highly ranked domain trigrams for long domainsmay be discarded to ensure that long domains, which are more likely tohave popular trigrams, do not skew the probabilities.

Maximum adjacent trigram probability—describes the probability oflocating adjacent popular trigram in the input string, in an embodiment.In a related embodiment, the feature may be calculated by calculatingthe probabilities of two adjacent trigrams based on the probabilities ofcomponent trigrams of the input string, as follows:

${{p_{t}\left( {{t_{j}(d)},{t_{j + 1}(d)}} \right)} = \frac{{{\hat{p}}_{t}\left( {t_{j}(d)} \right)} + {{\hat{p}}_{t}\left( {t_{j + 1}(d)} \right)}}{2}},$

Then the maximum of all the calculated probability values is computed todetermine the feature:

${m(d)} = {\max\limits_{j}{\left( {p_{t}\left( {{t_{j}(d)},{t_{j + 1}(d)}} \right)} \right).}}$

Suspicious trigrams metric—describes the number of unpopular trigramsfound in the input string, in an embodiment. In a related embodiment,the feature may be calculated by counting the number of componenttrigrams of the input string that have a value of zero for theirrespective ranking probability as calculated above:

n(d)=number of trigrams with {circumflex over (p)} _(t)(t(d))=0.

In a related embodiment, the feature analyzer 220 of HCS logic 200 mayperform block 330 to calculate complex features of packet flows. Featureanalyzer 220 may then store the complex features in datastore 260 inassociation with the corresponding packet flows.

4.3 Flow Level Classification

At block 335 of FIG. 3, packet flows of a batch are classified. In anembodiment, packet flows may be classified into malicious (M), possiblepolicy violation (PPV) and legitimate (L) packet flows. Theclassification is based on the calculated complex features and extractednative features that are compared to malicious network trafficproperties.

For example, a malicious network traffic that originates fromnon-browser may not have the “user agent” field set in the HTTP headersof packets of the flow. In response, the HCS logic 200 may analyze the“user agent” native flow feature of a packet flow and, if the field ismissing or set to a value indicating a non-recognizable browser, thenthe HCS logic 200 may classify the packet flow as “malicious non-browsertraffic” category and set the classification to “M.” Similarly, the HCSlogic 200 may examine the “MIME-type” native feature of a packet flow,and if the field is set to “application/x-bittorrent,” then the packetflow may be categorized as “torrent tracker” and classified as “PPV.” Onthe other hand, if the “MIME-type” native feature has value of“video/x-msvideo,” then the HCS logic 200 may classify the packet flowin a “multimedia (audio/video streaming)” category with “L” asclassification. In the examples above, for purposes of illustrating aclear example, only a single feature is used to classify a packet flow,however in practice multiple or all features of a packet flow are usedto classify a packet flow.

In an embodiment, complex features may be used to determine whether aURL string representing the whole URL or a partition of the URL from apacket flow is statistically similar to a URL string from a maliciousthreat. For example, malicious network traffic may contain URL stringsthat are computer-generated. Identifying the existence of such acomputer generated string in a URL of a packet flow may likely classifythe packet flow as malicious (M). A URL string may be identified ascomputer generated, if the string has a statistically randomdistribution of characters. In a related embodiment, random distributionof characters in a URL string may be identified by analyzing maximumcharacter/character type occurrence, consonant to vowel change ratio orany combination thereof. For example, if the consonant to vowel changeratio for a URL string is small, then the URL string may be computergenerated, and thus, the packet flow with the URL string may bemalicious.

In another embodiment, the URL string may be identified as originatingfrom a malicious network threat if the URL string is determined to bestatistically different from well-known URL strings. For example, mostwell-known URL strings are easy to pronounce and thus contain frequentlyused trigrams. In a related embodiment, complex features, such aspopular trigram probability, maximum adjacent trigram probability, thesuspicious trigrams metric or a combination thereof may represent thedegree of statistical similarity of the URL string with a malicious URLstring. For example, if the suspicious trigrams metric feature has alarge value, then the packet flow containing the URL string may beclassified as malicious.

In another embodiment, the HCS logic 200 may be configured to self-trainbased on known malicious and legitimate traffic on ranges of featurevalues that correspond respectively to malicious and legitimate packetflow feature values. In an embodiment, once the HCS logic 200 extractsor calculates features of a packet flow, the HCS logic may classify thepacket flow based on a comparison of its features with the known ranges.Table 2 lists example classifications and categories for a packet flowbased on its features, for an embodiment. In a related embodiment, apacket flow may be categorized into more than one category from Table 2.

TABLE 2 Classifications and categories for a packet flow ClassifierCategory M Data transfer through URL to computer- generated domain MDownloading file with multiple extensions M Data transfer through URL toraw IP address M Communication with generated domain M Data transferthrough URL M Download of malicious executable file M Maliciousnon-browser traffic M Malicious repetitive requests M WPAD (Web ProxyAuto-discovery Protocol) misuse PPV Torrent user PPV Remote desktop PPVTorrent tracker L Proxy connect L Connection check LStreaming/downloading data L Upload data through body L Skype user LFlash L Multimedia (audio/video streaming) L Software update L RSS feedL File download

Block 340 of FIG. 3 indicates repeating blocks 325 through 335associated with determining features of a packet flow and classifyingthe packet flow, for all packet flows in a batch. FIG. 4 depicts anexample of the result of classifying packet flows, in an embodiment. Abatch 415 of packet flows has been classified at flow level 420. Thepacket flows 425 represents a classified batch of packet flows of batch415. The classification of each packet flow within packet flows 425 isrepresented by different shadings.

In a related embodiment, classifier 240 may perform block 340 and maythen store the flow classifications in association with thecorresponding packet flows in datastore 260.

4.4 Event Description

At block 345 of FIG. 3, packet flows of a batch are selected into anevent set, in an embodiment. An event set may consist of packet flowsthat has been transmitted in response to an event on a computing device.Such event may represent one click of a user in a browser, or a singleaction of a malware item. Packet flows may be selected into an event setbased on a criterion. According to one embodiment, the criterion for theselection of packet flows may be the proximity of their respective timestamps, and packet flows with same or similar timestamp may be groupedinto a particular event set. In an alternative embodiment, packet flowsmay be grouped into a particular event set based on native features ofthe packet flows such as referrer. Other techniques for selecting packetflows into event sets may be implemented as described in U.S. patentapplication Ser. No. 14/519,160 (Attorney Docket No. P-533/US-993509)filed on Oct. 21, 2014.

At block 350, HCS determines event set features based on flow featuresof packet flows of an event set. The event set features may representconsistency, variability, and ranges of the flow features in the eventset. The event set feature values may be determined by applying variousfunctions to the flow feature values of the packet flows of the eventset. In an embodiment, aggregation functions such as minimum, maximum,average, standard deviation, may be applied.

In an embodiment, values for a particular complex flow feature may beaggregated to determine an event set feature. For a particular complexfeature, described in section 4.2, flow feature values of packet flowsof an event set may be selected. Then, an aggregation function may beapplied to the selected values to produce an event set feature. Forexample, a mean suspicious trigrams metric of an event set may bedetermined by averaging all the suspicious trigrams metric values ofpacket flows of the event set.

Similarly, a combination of aggregation functions may be used to derivean event set feature. An event set feature may be calculated by removingoutlier flow feature values based on the standard deviation of the flowfeature values. If a flow feature value is a number of standarddeviations away, then the flow feature value may be designated as anoutlier and not be used in calculation of an aggregation function todetermine an event set feature. Continuing with the above example usingthe mean suspicious trigrams metric, a standard deviation based on allthe suspicious trigrams metric flow feature values is first calculated;the flow feature values that are outside of three (3) standarddeviations from the mean metric value are disregarded. An adjusted meansuspicious trigrams metric event set feature is then calculated based onthe remaining flow feature values for the suspicious trigrams metric.However, the exact combination of aggregation functions is not critical.

In another embodiment, an event aggregation flow feature may bedetermined by selecting a common native flow feature. The value of theevent aggregation flow feature may be determined based on identifyingcommon native flow feature values of packet flows of an event set. Thecommon feature values may be determined based on the percentage of flowfeatures that have a same or similar value. For example, if a MIME-typenative feature has value of “video/x-msvideo” for all packet flows in anevent set, then an event set feature MIME-type is assigned to the value“video/x-msvideo”.

At block 355 of FIG. 3, an event set is classified into classes. Suchclasses may match packet flow classifications such as malicious (M),possible policy violation (PPV) and legitimate (L) events or may be moreor less granular. In an embodiment, an event set may be classified basedon event set feature by comparing event set features to known eventproperties. For example, an event set may be classified as malicious, ifa known malware has same or similar feature values. The malware isdetermined to have the same or similar features when statisticalanalysis of known malware features substantially matches the event setfeatures. Similarly, event set features may be compared to statisticalproperties of the legitimate network events to determine whether theevent set represents a legitimate event.

In a related embodiment, an event set may be classified withoutcomparison to known event properties. An event set feature may itselfrepresent an anomaly in a network traffic because the event set featuremay describe a discrepancy in flow feature values of packet flows of theevent set. For example, continuing with the above example using the meansuspicious trigrams metric, if the standard deviation of suspicioustrigrams metric values divided over the mean is below 1, then flowfeature values for the metric have low variance. Thus, the meansuspicious trigrams feature of the event set may be preciselyrepresentative of all packet flows and may classify the event as alegitimate or malicious by itself.

In another embodiment, an event set may be classified based on flowclassifications. An event set may be classified based on thenumber/percent of occurrences of different classifications within theclassifications of packet flows that are selected into the event set.For example, if majority of packet flows selected in an event set havebeen classified as malicious, then the event set is classified asmalicious. In another example, an event set may be classified based onthe worst case scenario and thus, if any packet flows is classified asmalicious in an event set, then the event set is classified asmalicious.

In another embodiment, flow features of packet flows may be the basisfor classifying an event set to which the packet flow belongs. When evenset features do not yield a conclusive classification, an event set maybe classified based on packet flow features. For example, if themajority of packet flows have various native flow features that areclassified as malicious, then regardless of values for the respectiveevent set features, the event set is classified as malicious.

At block 360 of FIG. 3, blocks 350 and 355 for determining features ofan event set and classifying the event set are repeated for all eventsets, in an embodiment. FIG. 4 depicts an example of the result forselecting packet flows into event sets and classifying the event sets.Event level 430 depicts the selection of packet flows 425 into eventsets 435 and the classification of event sets 435. Each event set inevent sets 435 is depicted by a rectangle, where the shading ofrectangle corresponds to the classification of the corresponding eventset. In an embodiment, with time 405, other batches of packet flows arereceived and classified, new events sets are aggregated on event level430 in accordance with blocks 350 and 355 of FIG. 3, in an embodiment.

In alternative embodiments, numerous event levels may exist based ontemporal aggregation of event sets and aggregation of the underlyingpacket flows of the event sets. For example, event sets may beaggregated into an hourly event set that comprises of all event setsthat have packet flows with timestamps that are within a particularhour-long duration. Such higher level event sets may have features andclassifications determined using the same techniques described herein.

In a related embodiment, aggregator 230 may perform block 345 to selectpacket flows into event sets. Feature analyzer 220 may perform block 350to determine event set features and then, may store the event setfeatures in datastore 260 in association with the corresponding eventsets. Classifier 240 may perform block 355 to classify event sets intoclassifications and then, may store the event set classification intodatastore 260 in associations with the corresponding event sets.

4.5 Incident Description

At block 365 of FIG. 3, an event set is assigned to a device group basedon an originating computing device and timestamp, in an embodiment. Eachtimestamp may be determined by the timestamps associated with the packetflows comprising the event set. The event sets from the same originatingdevice or multiple originating devices with the same user may beselected based on the timestamps of the event steps. A device group fora particular time period may contain all event sets from the originatingdevice(s) that are within the particular time period. Since each of theevent sets represents an event on the originating device(s), a devicegroup of event sets represents an incident on the originating device(s)during the particular time period. For example, an incident for past 24hours may be represented by a device group that comprises event setsfrom the originating device(s) that have timestamps within past 24 hoursfrom the current time. Similarly, other grouping of event sets into adevice group may be performed in other embodiments to represent otherincidents, however, the exact time period duration used to assign eventsets to a device group is not critical to the techniques describedherein.

At block 370, incident features for a device group are determined, in anembodiment. Incident features of a device group may be determinedsimilar to techniques described for event sets in section 4.4. Incidentfeatures of a device group may be calculated by statisticallyaggregating event set features of event sets of the group or flowfeatures of packet flows of the event sets of the group. Thus, theincident features may similarly represent consistency, variability, andranges of the event set features and the flow features corresponding tothe events on a computing device during a particular time period.

At block 375, a device group is classified into a particular incidentclassification, in an embodiment. The incident classifications may matchevent and packet flow classifications such as malicious (M), possiblepolicy violation (PPV) and legitimate (L). In a related embodiment, adevice group may have different incident classification which may behigher or lower granularity than the event set or flow classifications.For example, a device group may be classified into OK, warning, or faultincident classifications.

In an embodiment, a device group may be classified using techniquessimilar to an event set classification, such as those described insections 4.3 and 4.4. The device group may be classified based on thedetermined device group features. In a related embodiment, a devicegroup may be classified based on one or more event set features, eventset classifications, flow features and packet flow classifications orcombination thereof.

In an embodiment, at block 380, blocks 315 through 375 may be repeatedfor new batches. Thus, new incidents may be generated based on newevents received in the new batches following the same process of FIG. 3.For example, FIG. 4 depicts receiving a new batch every 5 minutes andincidents 450 may be classified every 5 minutes based on the new packetflows received. Since incident classifications may be performed peroriginating device, event sets from 435 that originated at device 1 areselected into device group 445, and event sets that originated at device2 are selected into device group 447 on device level 440. In analternative embodiment, if device 1 and device 2 are determined to havethe same user, the event sets from 435 may be selected into a singledevice group.

Incidents 450 comprise incidents, such as incident 455, which have atime period duration of 24 hours and are created every 5 minutes.Incidents 450 are classified based on corresponding device groups andthe device groups' event sets and packet flows. For example, incident455 is classified based on device group 445 and based on event sets fromevent set 435 and packet flows from packet flows 425 from batch 415. Asnew batches of packet flows are received, new incidents are createdthat, depending on time period, may or may not include previousincidents event sets and packet flows. Thus, the new incidents may havedifferent classifications as depicted by incidents 450.

In a related embodiment, aggregator 230 may perform block 365 to assignevent sets to device groups and may store them in datastore 260. Featureanalyzer 220 may perform block 370 to determine incident features andthen, may store the incident features in datastore 260 in associationwith the corresponding device groups or may directly pass the incidentfeatures to classifier 240. Classifier 240 may perform block 375 toclassify device groups into incident classes. Classifier 240 may thenstore the incident classifications into datastore 260 in associationswith the corresponding device groups.

5.0 IMPLEMENTATION MECHANISMS Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 6 is a block diagram that illustrates a computersystem 600 upon which an embodiment of the disclosure may beimplemented. Computer system 600 includes a bus 602 or othercommunication mechanism for communicating information, and a hardwareprocessor 604 coupled with bus 602 for processing information. Hardwareprocessor 604 may be, for example, a general purpose microprocessor.

Computer system 600 also includes a main memory 606, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 602for storing information and instructions to be executed by processor604. Main memory 606 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 604. Such instructions, when stored innon-transitory storage media accessible to processor 604, rendercomputer system 600 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 600 further includes a read only memory (ROM) 608 orother static storage device coupled to bus 602 for storing staticinformation and instructions for processor 604. A storage device 610,such as a magnetic disk or optical disk, is provided and coupled to bus602 for storing information and instructions.

Computer system 600 may be coupled via bus 602 to a display 612, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 614, including alphanumeric and other keys, is coupledto bus 602 for communicating information and command selections toprocessor 604. Another type of user input device is cursor control 616,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 604 and forcontrolling cursor movement on display 612. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 600 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 600 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 600 in response to processor 604 executing one or more sequencesof one or more instructions contained in main memory 606. Suchinstructions may be read into main memory 606 from another storagemedium, such as storage device 610. Execution of the sequences ofinstructions contained in main memory 606 causes processor 604 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperation in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 610.Volatile media includes dynamic memory, such as main memory 606. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 602. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 604 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 600 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 602. Bus 602 carries the data tomain memory 606, from which processor 604 retrieves and executes theinstructions. The instructions received by main memory 606 mayoptionally be stored on storage device 610 either before or afterexecution by processor 604.

Computer system 600 also includes a communication interface 618 coupledto bus 602. Communication interface 618 provides a two-way datacommunication coupling to a network link 620 that is connected to alocal network 622. For example, communication interface 618 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 618 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 618sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 620 typically provides data communication through one ormore networks to other data devices. For example, network link 620 mayprovide a connection through local network 622 to a host computer 624 orto data equipment operated by an Internet Service Provider (ISP) 626.ISP 626 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 628. Local network 622 and Internet 628 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 620and through communication interface 618, which carry the digital data toand from computer system 600, are example forms of transmission media.

Computer system 600 can send messages and receive data, includingprogram code, through the network(s), network link 620 and communicationinterface 618. In the Internet example, a server 630 might transmit arequested code for an application program through Internet 628, ISP 626,local network 622 and communication interface 618.

The received code may be executed by processor 604 as it is received,and/or stored in storage device 610, or other non-volatile storage forlater execution.

6.0 EXTENSIONS AND ALTERNATIVES

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. Thus, the sole and exclusive indicatorof what is the disclosure, and is intended by the applicants to be thedisclosure, is the set of claims that issue from this application, inthe specific form in which such claims issue, including any subsequentcorrection. Any definitions expressly set forth herein for termscontained in such claims shall govern the meaning of such terms as usedin the claims. Hence, no limitation, element, property, feature,advantage or attribute that is not expressly recited in a claim shouldlimit the scope of such claim in any way. The specification and drawingsare, accordingly, to be regarded in an illustrative rather than arestrictive sense.

What is claimed is:
 1. A computer system comprising: one or more networkinterfaces that are configured to couple to a data network and toreceive a plurality of packet flows therefrom; one or more processorscoupled to the one or more network interfaces; an aggregator that isconfigured to select, based on a criterion, one or more selected packetflows from the plurality of packet flows and placing the selected packetflows into a set; a feature analyzer that is configured to determine,for each packet flow in the set, a flow feature associated with thatpacket flow based on data from that packet flow, and classifying eachpacket flow into a flow class based on the flow feature; the featureanalyzer that is further configured to determine a set feature for theset based on one or more of the flow features that are associated withthe selected packet flows of the set; a classifier that is configured toclassify the set into a set class based on the set feature; and a threatreporter that is configured to report, based on the set class, a threatincident on a computing device originating the selected one or more oneor more packet flows.
 2. The system of claim 1, wherein the featureanalyzer is further configured to determine the flow feature based onany combination of one or more of: URL data from said each packet flow,flow duration of said each packet flow, number of bytes transferred insaid each packet flow, type of said each packet flow, status of saideach packet flow, referrer of said each packet flow, timestampsassociated with said each packet flow, internet address of the computingdevice originating said each packet flow, type of the computing deviceoriginating said each packet flow, or status of the computing deviceoriginating said each packet flow.
 3. The system of claim 1, wherein thefeature analyzer is further configured to determine the set feature bycalculating an aggregate function of the one or more of the flowfeatures.
 4. The system of claim 1, wherein the classifier is furtherconfigured to determine the set class based on the one or more of theflow features or flow classes that the selected packet flows of the setare classified into.
 5. The system of claim 1, wherein the criterion isbased on the selected packet flows that are associated with a particularevent on the computing device.
 6. The system of claim 5, wherein theparticular event is an action performed on the computing device.
 7. Thesystem of claim 1, wherein: the aggregator is further configured toselect, into a group, one or more sets of packet flows that include theset, based on temporal attributes or native features of particularpacket flows of the one or more sets of packet flows that originated atthe computing device; the feature analyzer is further configured todetermine a group feature for the group based on one or more setfeatures corresponding to the one or more sets; and the classifier isfurther configured to classify the group into a group class based on thegroup feature.
 8. The system of claim 7, wherein the feature analyzer isfurther configured to determine the group feature by calculating anaggregate function of the one or more set features.
 9. The system ofclaim 7, wherein the classifier is further configured to determine thegroup class based on any combination of one or more of: the one or moreset features, particular one or more flow features corresponding to theparticular packet flows of the one or more sets, set classes that theone or more sets are classified into, or particular flow classes thatthe particular packet flows are classified into.
 10. The system of claim7, wherein the temporal attributes are timestamps of the particularpacket flows.
 11. The system of claim 10, wherein the timestamps of theparticular packet flows are within a particular time duration.
 12. Amethod comprising: receiving a plurality of packet flows from a datanetwork; selecting, based on a criterion, one or more selected packetflows from the plurality of packet flows and placing the selected packetflows into a set; determining, for each packet flow in the set, a flowfeature associated with that packet flow based on data from that packetflow, and classifying each packet flow into a flow class based on theflow feature; determining a set feature for the set based on one or moreof the flow features that are associated with the selected packet flowsof the set; classifying the set into a set class based on the setfeature; based on the set class, reporting a threat incident on acomputing device originating the selected one or more one or more packetflows; and wherein the method is executed by one or more computingdevices.
 13. The method of claim 12, further comprising determining theflow feature based on any combination of one or more of: URL data fromsaid each packet flow, flow duration of said each packet flow, number ofbytes transferred in said each packet flow, type of said each packetflow, status of said each packet flow, referrer of said each packetflow, timestamps associated with said each packet flow, internet addressof the computing device originating said each packet flow, type of thecomputing device originating said each packet flow, or status of thecomputing device originating said each packet flow.
 14. The method ofclaim 12, further comprising determining the set feature by calculatingan aggregate function of the one or more of the flow features.
 15. Themethod of claim 12, further comprising determining the set class basedon the one or more of the flow features or flow classes that theselected packet flows of the set are classified into.
 16. The method ofclaim 12, wherein the criterion is based on the selected packet flowsthat are associated with a particular event on the computing device. 17.The method of claim 16, wherein the particular event is an actionperformed on the computing device.
 18. The method of claim 12, furthercomprising: selecting, into a group, one or more sets of packet flowsthat include the set, based on temporal attributes or native features ofparticular packet flows of the one or more sets of packet flows thatoriginated at the computing device; determining a group feature for thegroup based on one or more set features corresponding to the one or moresets; and classifying the group into a group class based on the groupfeature.
 19. The method of claim 18, further comprising determining thegroup feature by calculating an aggregate function of the one or moreset features.
 20. The method of claim 18, further comprising determiningthe group class based on any combination of one or more of: the one ormore set features, particular one or more flow features corresponding tothe particular packet flows of the one or more sets, set classes thatthe one or more sets are classified into, or particular flow classesthat the particular packet flows are classified into.
 21. The method ofclaim 18, wherein the temporal attributes are timestamps of theparticular packet flows.
 22. The method of claim 21, wherein thetimestamps of the particular packet flows are within a particular timeduration.